Combining neural networks and pattern matching for ontology mining - a meta learning inspired approach

Several applications dealing with natural language text involve automated validation of the membership in a given category (e.g. France is a country, Gladiator is a movie, but not a country). Meta-learning is a recent and powerful machine learning approach, which goal is to train a model (or a family of models) on a variety of learning tasks, such that it can solve new learning tasks in a more efficient way, e.g. using smaller number of training samples or in less time. We present an original approach inspired by meta-learning and consisting of two tiers of models: for any arbitrary category, our general model supplies high confidence training instances (seeds) for our category-specific models. Our general model is based on pattern matching and optimized for the precision at top N, while its recall is not important. Our category-specific models are based on recurrent neural networks (RNN-s), which recently showed themselves extremely effective in several natural language applications, such as machine translation, sentiment analysis, parsing, and chatbots. By following the meta-learning principles, we are training our highest level (general) model in such a way that our second-tier category-specific models (which are dependent on it) are optimized for the best possible performance in a specific application. This work is important because our approach is capable of verifying membership in an arbitrary category defined by a sequence of words including longer and more complex categories such as Ridley Scott movie or City in southern Germany that are currently not supported by existing manually created ontologies (such as Freebase, Wordnet or Wikidata). Also, our approach uses only raw text, and thus can be useful when there are no such ontologies available, which is a common situation with languages other than English. Even the largest English ontologies are known to have low coverage, insufficient for many practical applications such as automated question answering, which we use here to illustrate the advantages of our approach. We rigorously test it on a number of questions larger than the previous studies and demonstrate that when coupled with a simple answer-scoring mechanism, our meta-learning-inspired approach 1) provides up to 50% improvement over prior approaches that do not use any manually curated knowledge bases and 2) achieves the state ofthe- art performance among all the current approaches including those taking advantage of such knowledge bases

Sensor data fusion for the industrial artificial intelligence of things

The emergence of smart sensors, artificial intelligence, and deep learning technologies yield artificial intelligence of things, also known as the AIoT. Sophisticated cooperation of these technologies is vital for the effective processing of industrial sensor data. This paper introduces a new framework for addressing the different challenges of the AIoT applications. The proposed framework is an intelligent combination of multi-agent systems, knowledge graphs and deep learning. Deep learning architectures are used to create models from different sensor-based data. Multi-agent systems can be used for simulating the collective behaviours of the smart sensors using IoT settings. The communication among different agents is realized by integrating knowledge graphs. Different optimizations based on constraint satisfaction as well as evolutionary computation are also investigated. Experimental analysis is undertaken to compare the methodology presented to state-of-the-art AIoT technologies. We show through experimentation that our designed framework achieves good performance compared to baseline solutions.publishedVersio

Learning models for semantic classification of insufficient plantar pressure images

Establishing a reliable and stable model to predict a target by using insufficient labeled samples is feasible and effective, particularly, for a sensor-generated data-set. This paper has been inspired with insufficient data-set learning algorithms, such as metric-based, prototype networks and meta-learning, and therefore we propose an insufficient data-set transfer model learning method. Firstly, two basic models for transfer learning are introduced. A classification system and calculation criteria are then subsequently introduced. Secondly, a dataset of plantar pressure for comfort shoe design is acquired and preprocessed through foot scan system; and by using a pre-trained convolution neural network employing AlexNet and convolution neural network (CNN)- based transfer modeling, the classification accuracy of the plantar pressure images is over 93.5%. Finally, the proposed method has been compared to the current classifiers VGG, ResNet, AlexNet and pre-trained CNN. Also, our work is compared with known-scaling and shifting (SS) and unknown-plain slot (PS) partition methods on the public test databases: SUN, CUB, AWA1, AWA2, and aPY with indices of precision (tr, ts, H) and time (training and evaluation). The proposed method for the plantar pressure classification task shows high performance in most indices when comparing with other methods. The transfer learning-based method can be applied to other insufficient data-sets of sensor imaging fields

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions